Given the discrete nature of CO problems, a natural approach is to pose them as reinforcement learning (RL) problems [1]}. The aim is then to learn a policy that selects the best actions to maximize a reward directly related to the optimization objective. Algorithms then differ in the way the policy is parameterized: either in an end-to-end manner where the actions directly correspond to solutions of the optimization problem [2]}, [3]}, [4]}, or in a hybrid manner, where the policy augments parts of a traditional solver, e.g. by replacing heuristics used in setting parameters of an algorithm, see e.g. [5]}, [6]}, [7]}, [8]}. Our approach follows an end-to-end design philosophy, which, not having to rely on an external algorithm, affords better control of post-compile run time and facilitates application on edge devices [8]}. Furthermore, RL has the advantage of being useful as a black box optimizer, when no handcrafted heuristics can be designed.
